Statistics in Early Childhood and Primary Education

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Early Mathematics Learning and Development Aisling Leavy  Maria Meletiou-Mavrotheris   Efi Paparistodemou Editors Statistics in Early Childhood and Primary Education Supporting Early Statistical and Probabilistic Thinking Early Mathematics Learning and Development Series Editor Lyn D English Queensland University of Technology, School of STM Education Brisbane, QLD, Australia More information about this series at Aisling Leavy Maria Meletiou-Mavrotheris Efi Paparistodemou • Editors Statistics in Early Childhood and Primary Education Supporting Early Statistical and Probabilistic Thinking 123 Editors Aisling Leavy Department of STEM Education Mary Immaculate College, University of Limerick Limerick, Ireland Efi Paparistodemou Cyprus Pedagogical Institute Latsia, Nicosia, Cyprus Maria Meletiou-Mavrotheris Department of Education Sciences European University Cyprus Nicosia, Cyprus ISSN 2213-9273 ISSN 2213-9281 (electronic) Early Mathematics Learning and Development ISBN 978-981-13-1043-0 ISBN 978-981-13-1044-7 (eBook) Library of Congress Control Number: 2018945073 © Springer Nature Singapore Pte Ltd 2018 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore To Sercan, Róisín and Deren who will be disappointed to find this book is not about wizards, dragons or fictional characters & To Stathis, Nikolas, and Athanasia for giving me the power to embrace the uncertain future with curiosity and optimism & Panayiotis, Christoforos and Despina for creating chances Foreword Educate a child according to his way: even as he grows old he will not depart from it Proverbs 22, In the era of data deluge, people are no longer passive recipients of data-based reports They are becoming active data explorers who can plan for, acquire, manage, analyse, and infer from data The goal is to use data to understand and describe the world and answer puzzling questions with the help of data analysis tools and visualizations Being able to provide good evidence-based arguments and critically evaluate data-based claims are important skills that all citizens should have and, therefore, that all students should learn as part of their formal education Statistics is therefore such a necessary and important area of study Moore (1998) suggested that it should be viewed as one of the liberal arts and that it involves distinctive and powerful ways of thinking He wrote: “Statistics is a general intellectual method that applies wherever data, variation, and chance appear It is a fundamental method because data, variation, and chance are omnipresent in modern life” (p 134) Understanding the powers and limitations of data is key to active citizenship and to the prosperity of democratic societies It is not surprising therefore that statistics instruction at all educational levels is gaining more students and drawing more attention Today’s students need to learn to work and think with data and chance from an early age, so they begin to prepare for the data-driven society in which they live This book is therefore a timely and important contribution in this direction This book provides a useful resource for members of the mathematics and statistics education community that facilitates the connections between research and practice The research base for teaching and learning statistics and probability has been increasing in size and scope, but has not always been connected to teaching practice nor accessible to the many educators teaching statistics and probability in early childhood and primary education Despite the recognized importance of vii viii Foreword developing young learners’ early statistical and probabilistic reasoning and conceptual understanding, the evidence base to support such a development is rare By focusing on this important emerging area of research and practice in early childhood (ages 3–10), this publication fills a serious gap in the literature on the design of probability and statistics meaningful experiences into early mathematics teaching and learning practices It informs best practices in research and teaching by providing a detailed account of comprehensive overview of up-to-date international research work on the development of young learners’ reasoning with data and chance in formal, informal, and non-formal education contexts The book is also an important contribution to the growth of statistics education as a recognized discipline Only recently, the first International Handbook of Research in Statistics Education has been published (Ben-Zvi, Makar, & Garfield, 2018), signifying that statistics education has matured to become a legitimate field of knowledge and study This current book provides another brick in building the solid foundation of the emerging discipline by providing a comprehensive survey of state-of-the-art knowledge, and of opportunities and challenges associated with the early introduction of statistical and probabilistic concepts in educational settings By providing valuable insights into contemporary and future trends and issues related to the development of early thinking about data and chance, this publication will appeal to a broad audience that includes not only mathematics and statistics education researchers, but also teaching practitioners It is not often that a book serves to synthesize an emerging field of study while at the same time meeting clear practical needs: educate a child during his early years with powerful ideas in statistics and probability even at an informal level, and even as he grows old he will not depart from it It is a deep pleasure to recommend this pioneering and inspiring volume to your attention Haifa, Israel Dani Ben-Zvi The university of Haifa References Ben-Zvi, D., Makar, K., & Garfield J (Eds.) (2018) International handbook of research in statistics education Springer international handbooks of education Springer Cham Moore D S (1998) Statistics among the Liberal Arts Journal of the American Statistical Association, 93(444), 1253–1259 Preface Introduction New values and competencies are necessary for survival and prosperity in the rapidly changing world where technological innovations have made redundant many skills of the past The expanding use of data for prediction and decisionmaking in almost all domains of life has made it a priority for mathematics instruction to help all students develop their statistical and probabilistic reasoning (Franklin et al., 2007) Despite, however, the introduction of statistics in school and university curricula, the research literature suggests poor statistical thinking among most college-level students and adults, including those who have formally studied the subject (Rubin, 2002; Shaughnessy, 1992) In order to counteract this and achieve the objective of a statistically literate citizenry, leaders in mathematics education have in recent years being advocating a much wider and deeper role for probability and statistics in primary school mathematics, but also prior to schooling (Shaughnessy, Ciancetta, Best, & Canada, 2004; Makar & Ben-Zvi, 2011) It is now widely recognized that the foundations for statistical and probabilistic reasoning should be laid in the very early years of life rather than being reserved for secondary school level or university studies (National Council of Teachers of Mathematics, 2000) As the mathematics education literature indicates, young children possess an informal knowledge of mathematical concepts that is surprisingly broad and complex (Clements & Sarama, 2007) Although the amount of research on young learners’ reasoning about data and chance is still relatively small, several studies conducted during the past decade have illustrated that when given the opportunity to participate in appropriate, technology-enhanced instructional settings that support active knowledge construction, even very young children can exhibit wellestablished intuitions for fundamental statistical concepts (e.g Bakker, 2004; English, 2012; Leavy & Hourigan, 2018; Makar, 2014; Makar, Fielding-Wells & Allmond, 2011; Meletiou-Mavrotheris & Paparistodemou, 2015; Paparistodemou & Meletiou-Mavrotheris, 2008; Rubin, Hammerman, & Konold, 2006) Use of ix x Preface appropriate educational tools (e.g dynamic statistics software), in combination with suitable curricula and other supporting material, can provide an inquiry-based learning environment through which genuine endeavours with data can start at a very young age (e.g Ben-Zvi, 2006; Gil & Ben-Zvi, 2011; Hourigan & Leavy, 2016; Leavy, 2015; Leavy & Hourigan, 2015, 2018; Paparistodemou & Meletiou-Mavrotheris, 2010; Pratt, 2000) Through the use of meaningful contexts, data exploration, simulation, and dynamic visualization, young children can investigate and begin to comprehend abstract statistical concepts, developing a strong conceptual base on which to later build a more formal study of probability and statistics (Hall, 2011; Ireland & Watson, 2009; Konold & Lehrer, 2008; Leavy & Hourigan, 2016, 2018; Meletiou-Mavrotheris & Paparistodemou, 2015) Edited Volume Objectives The edited volume will contribute to the Early Mathematics Learning and Development Book Series, a volume focused on the development of young children’s (ages 3–10) understanding of data and chance, an important yet neglected area of mathematics education research The goal of this publication is to inform best practices in early statistics education research and instruction through the provision of a detailed account of current best practices, challenges, and issues, and of future trends and directions in early statistical and probabilistic learning worldwide Specifically, the book has the following objectives: Provide a comprehensive overview of up-to-date international research work on the development of young learners’ reasoning about data and chance in formal, informal, and non-formal education contexts; Identify and publish worldwide best practices in the design, development, and educational use of technologies (mobile apps, dynamic software, applets, etc.) in support of children’s early statistical and probabilistic thinking processes and learning outcomes; Provide early childhood educators with a wealth of illustrative examples, helpful suggestions, and practical strategies on how to address the challenges arising from the introduction of statistical and probabilistic concepts in preschool and school curricula; Contribute to future research and theory building by addressing theoretical, epistemological, and methodological considerations regarding the design of probability and statistics learning environments targeting young children; and Account for issues of equity and diversity in early statistical and probabilistic learning, so as to ensure increased participation of groups of children at special risk of exclusion from math-related fields of study and careers This timely publication approaches an audience that is broad enough to include all researchers and practitioners interested in the development of children’s understanding of data and chance in the early years of life Early childhood educators can 17 Young Children’s Statistical Literacy in Modelling with Data … 299 to appreciate variation and its relationship to expectation/prediction needs to begin early with appropriate hands-on experiences and student/teacher questioning In the remainder of this chapter, I first describe two investigations, one dealing with statistics and the other with probability Of particular interest in both activities were students’ identification of variation, their informal inferential reasoning, the representations they created, and how they interpreted their models Findings related to these aspects are presented 17.5 Investigations in Modelling with Data and Chance 17.5.1 Background The two investigations, Manufacturing Licorice, and What is the Chance of That? were implemented during the first year of a four-year longitudinal study being conducted across grades three through six in two Australian capital cities (with collaborators, Jane Watson and Noleine Fitzallen) The first investigation was implemented in both cities, while the second in just the author’s city Examples of children’s responses are drawn from data in the author’s city only (one third-grade classroom, mean age of 8.8 years) The first investigation, Manufacturing Licorice, was implemented in the first half of the third-grade year, with the second, What is the Chance of That? in the second half For the implementation of each investigation, “focus groups” were selected in consultation with the class teacher and comprised three students of mixed achievement levels Detailed lesson plans for both investigations were prepared for the teacher, as was a workbook for each student The students worked in small groups of mostly three members, although recorded their own responses to the investigations in their workbooks 17.5.2 Design and Analysis A design-based approach was adopted (Cobb, Jackson, & Dunlap, 2016), with such an approach catering for complex classroom situations that contain many variables and real-world constraints A design-based approach supports learning and informs future learning experiences based on feedback, and facilitates contributions to both theory and practice Data collection included videotaping of three focus groups as they worked the investigations, as well as all class discussions, which were subsequently transcribed for analysis The data reported in this chapter are drawn from the students’ workbooks, together with the recorded and transcribed group work and whole-class discussions In conjunction with an experienced research assistant, content analysis (Patton, 2002) was applied in initially identifying and coding the data recorded in 300 L D English the students’ workbooks A further round of refined coding was undertaken to ensure meaningfulness and accuracy For example, in coding the students’ responses for “What does the shape of the class plot tell you about the variation in the licorice sticks made by the class?”, we refined our coding thus: “code 2: “student must refer to change or comparison, such as ‘there is more on 10 and less on 7’; ‘there are a lot of people between and 16’”; code 1: “student refers to a single characteristic (no reference to variation), such as ‘there is a lot on 10; there is a lot on 13’”; and code 0: “the student gave no response, an idiosyncratic response, or one that was out of context” such as, ‘there are numbers, g, and sticky notes’; they are both like long rectangles’” Iterative refinement cycles for videotape analyses of conceptual change (Lesh & Lehrer, 2000) were applied in reviewing the transcribed focus group and whole-class discussions to gain greater insights into the development of the students’ learning 17.5.3 Investigation Implementation Manufacturing Licorice In this investigation, students experienced the “creation of variation” as they compared the masses of “licorice sticks” they made by hand (using Play-Doh) with those made using a Play-Doh extruder kit (“factory made”; adapted from Watson, Skalicky, Fitzallen, & Wright, 2009) Students chose their own forms of representation in displaying their models for the two forms of licorice production and identified, compared, and explained the features of their data distributions Following a number of introductory experiences (e.g exploring the manufacturing processes and roles of engineers in the creation of licorice and other such products), the students discussed questions pertaining to quality control and the overall manufacturing process In small groups, the students then undertook the two investigations involving handmade licorice sticks and those made by the Play-Doh extruder For each licorice manufacturing method, the students identified, measured, compared, and recorded attributes of the sticks including their mass, and compared their findings with their group members Their group data on the masses were then collated and compared within the group Each group member then created her own representation of the collated group data No direct guidance was provided for developing these representations However, if a student’s representation was incomplete or unclear we would remind them to check that their creation could be interpreted clearly Subsequent class sharing and interpreting of the resultant group models for each method enabled students to identify the range and “typical” masses displayed in each group model Each method of licorice manufacture ended with all group data being collated and displayed as a class plot (bar graph) Figure 17.2 illustrates the plots created as each child placed a post-it-note, on which they had recorded one of their licorice stick masses, in the appropriate position on the axis drawn on the class whiteboard The students explored and discussed the data distribution revealed in the whole-class model for each licorice manufacturing method, with inferences drawn regarding the two methods and the predicted masses if further sticks were made 17 Young Children’s Statistical Literacy in Modelling with Data … 301 What is the Chance of That? In this investigation, the children created their own chance experiments and independently developed core probability understandings Introductory experiences included reading a chance storybook [“Probably Pistachio” (Murphy, 2001)], which helped children appreciate that one cannot predict with certainty the outcomes of a probabilistic situation On reading the book, the children recorded examples of events that would be certain to happen for them the following weekend, could possibly happen, and would be impossible to occur The next introductory component engaged the children in playing a “bingo” game where the notions of randomness and variation in chance events were experienced On playing the game, the children responded to questions including, “Did everyone have an equal chance of winning?”; “Was it certain that someone would win?”; “Was it possible for two people to win?”; and “Do you think some numbers are more likely to roll out than others?” They were to justify their responses In the main investigation, the children were presented the scenario of a company seeking their help in designing a game (“What is the chance of that?”) The children were to help the company determine the chances of selecting various coloured counters from a “mystery bag” A container of 36 counters comprising nine of each of four different colours was presented to each group of children Each group was to select only 12 of the 36 coloured counters to place in their group’s mystery bag, using their choice of numbers of each colour but ensuring there was at least one of every colour in the bag (the numbers of each colour did not have to be the same) The remaining counters were returned to the container Each group member recorded on a table the number of each coloured counter they chose for their mystery bag of 12 counters Prior to selecting counters, each child was to predict and record what coloured counter she would draw from the bag if she only had one chance and was not permitted to look Once all students in the group had made their prediction, each child recorded the predictions of the other group members Next, the children took turns in selecting one counter without looking, returning the counter to the bag, and recording the outcome of each group member’s selection On completion, they responded to questions (and justified their responses) on their initial predictions and any variation in the data they observed The questions included: “Why did you predict you would select that colour?”; “Did you select the coloured counter that you predicted?”; “Describe any variation you can see in your table of data”; and “If you were to repeat the counter selection over and over, how might the data in your table change?” The following questions were then posed regarding the children’s chances of selecting counters from their bag (responses were again to be justified): “Does each counter have an equal chance of being selected?”; “Is there a coloured counter that has the greatest chance of being selected?”; “Is there a coloured counter that has the least chance of being selected?”; and “Would it be possible to select a purple counter from your bag?” (no purple counters were included in the containers of counters) The last component of the investigation engaged the children in creating two representations of the chances of selecting the different coloured counters from their group’s mystery bag The first representational form linked the children’s learning with their introduction to fractions earlier in the year (e.g “2 chances out of 12 302 L D English chances;” 12 ) The second form was primarily a statistical representation of the children’s own choice On creation of their models, the children were to explain how their model displayed the chances of selecting their coloured counters Following this, the children were invited to represent their chances in a different way (rerepresenting) and subsequently compare their two models to determine which they considered conveyed their “chance story” more effectively, and why In reporting a sample of findings from both investigations, consideration is given to their shared features of identifying variation, drawing informal inferences, creating representations, and interpreting models generated 17.6 Sample of Findings 17.6.1 Identifying Variation Manufacturing Licorice The majority of students were able to identify variation and justify their responses Over 80% of students (N 23) could detect the variation in the mass of handmade licorice sticks and all (N 24) could so for the “factory made” ones Likewise, the students had few difficulties in giving an appropriate initial reason for this variation in the former (87%, N 23), with explanations including reference to some sticks being “fatter” or “too thin” or “thicker” A considerable number of students (71%, N 24) could explain that the Play-Doh extruder was more accurate in producing sticks of a consistent mass (e.g., “Because it’s a machine like, the machine makes them all about the same size and when you’re doing them with your hands you can’t really tell if they’re going to be the same size or not”) What is the Chance of That? As for the previous investigation, the students had little difficulty in identifying variation in their predictions and outcomes of counter selection All students except one (N 24) were able to explain the variation in the table displaying their predictions and outcomes The students’ responses varied from “We all got the same colour which was yellow so there is no variation”, to “There wasn’t much variation because we had reds and of (each) of the other colours”, and “Yes (there was variation) because we picked out different colours and predicted different colours” One student referred to variation in the student names and predictions (“On the table the names are different and the predictions are different”) Students’ overall ability to identify variation in both investigations provided a foundation for drawing informal inferences, where one has to acknowledge variation in the data, and hence the uncertainty with which any conclusions can be drawn (cf Makar, Bakker, & Ben-Zvi, 2011; Lehrer & English, 2018) As noted next, students’ reference to chance in drawing conclusions from the Manufacturing Licorice investigation suggests they were linking their understanding of statistics and probability in developing statistical literacy 17 Young Children’s Statistical Literacy in Modelling with Data … 303 17.6.2 Drawing Inferences On both investigations, students were asked to make informal inferences from the data created and/or the models generated Manufacturing Licorice In this investigation, students drew inferences from the whole-class models constructed from the group data collected for each manufacturing method The scenario was posed: “If you made one more piece of licorice, what you think (predict) its mass might be? How did you decide?” Students were readily able to respond to the first part of this question, with 88% (N 24) identifying an appropriate mass range for the handmade and 96% for the equipment-made sticks The majority of students could also offer appropriate reasons for each decision, referring to either their own data or the whole-class data Their reasons included, “I think because most of mine were around ten and mine were both exactly cm wide and cm long”; “because it is about the average”; and “I decided because 13 g is the typical mass of sticks in the class” As part of a follow-up class discussion, the students were also asked, “If another student came into our class and made some licorice, what you think hers would be (mass of licorice stick)?” In their responses, the students frequently referred to chance and uncertainty when explaining what the mass of a licorice stick made by a new student might be For example, one student explained that, “It might be 13 because most people got … 13 so maybe that’s the typical number” Another explained, “I think maybe 12, because if she came in, there’s a chance, because the Fun Factory makes all of them um pretty similar and, … but I decided on that [13 g] because I think there’s a more likely chance that she would [make that mass] because it won’t always be bigger, she might get it a little smaller than some” The teacher asked a further question, namely, “Would you expect, say, if we did it again next week and we used the same Play-Doh, and we used the same Fun Factory, would you expect the same plots (i.e the same class plots of the two licorice-making methods)?” Alesha expressed the opinion, “I think they might be different because like we could something, we may have like cut it a bit further or because it’s really hard to get everything exact, so it won’t always be exact” Monica agreed, “…maybe or maybe not, I sort of agree … you actually don’t know because … when you made three of them like last week they weren’t all the same mass, they weren’t all 15 or they weren’t all 13…” What is the Chance of That? As mentioned earlier in this section, children were asked to predict the colours they might select from their bag of counters and give reasons for their answer The students were also asked if their predictions would guarantee the outcomes The students were readily able to justify their predictions based on the proportions of counters in their bags, with 75% (N 24) offering reasons such as “I predicted green because there were green counters and all the other colours were less” One student simply referred to a random selection: “My prediction was yellow I chose it because I randomly chose”, while 21% offered a general reason unrelated to chance notions or an irrelevant response, such as, “I thought I would get green because I practised it in random and I got green and 304 L D English because it is one of my favourite colours” Another student also referred to predicting her favourite colour, while another explained, “I predicted blue because when the counters were in a pile blue was on top so when we put it in [the bag] it would still be on top” Almost all students were able to give appropriate reasons for why their predictions would not guarantee the outcomes, with comments such as: “No, because we have an equal chance of getting each colour because there are three of each colour”, and “I can’t be certain that I will always pick a green counter but it may be likely that I will pick a green or blue” One student wrote, “I guarantee I will get a colour I don’t guarantee that I will get silver” [there were no silver counters] 17.6.3 Creating Representations and Interpreting Resultant Models Manufacturing Licorice Perhaps not surprising, given the typical nature of early data experiences in their curriculum, the children mostly created bar graphs to display their licorice-making results Two students in two groups, however, used a threeway table (Fig 17.1), with one student using both tallies and a three-way table to represent her data As can be seen in Fig 17.1, the former student also indicated the frequencies of some of the masses Although the children favoured bar graphs, they differed in their approaches to organizing and structuring their data For example, many students (78%, N 23) structured their data according to each group member’s results (e.g Monica, Kate, Sarah), while some (13%) ordered the data differently, such as from the “biggest licorice” to “second biggest”, to “second smallest”, to “smallest licorice” as illustrated in Fig 17.2 One student displayed each member’s heaviest licorice stick only On collating the group results to form a class plot for each licorice-making method (see Fig 17.3), the students were to describe the data distributions of each model Sixty-two per cent of the students (N 24) could identify one feature of the model for the handmade licorice, (e.g “very, very lumpy”; “zig-zag”), while 33% of these students could identify multiple characteristics (e.g “lots of spaces and humps and sections and a lot at the start”) In contrast, all students except one were able to describe the class model developed for the second method, with 79% (N 24) identifying multiple features 17 Young Children’s Statistical Literacy in Modelling with Data … 305 Fig 17.1 Three-way table displaying one group member’s masses The students’ comparisons of the two class plots further suggested their development of statistical literacy as they experimented with the two licorice-making methods (e.g “handmade was squished together but factory made are apart; factory made looks like a bed to me but handmade looks like boxes in a storage room; handmade are more horizontal but factory made is more vertical The typical number for handmade was 11 g but in factory made it was 13 g”) What is the Chance of That? In contrast to the Manufacturing Licorice representations, the students created a range of ways to display their chances of selecting the counters in their bags Furthermore, their inscriptions and explanations indicated a linking of their understanding of statistics, probability, and rational number Table 17.2 displays the forms of representation the children produced for their first and second representations It is interesting to note the prevalence of children’s use of circle graphs, even though they had not been taught these formally nor had they been introduced to fraction representations using this format (similar findings regarding children’s independent use of circle graphs were observed in earlier studies, e.g English, 2014) Bar graphs were also popular but less so than in the previous investigation The students increased substantially their use of circle graphs for their second representations; although not observed, it could be that some children had learned about the use of circle graphs from their peers in the first representation As indicated in Table 17.2, fewer students created bar graphs for their second representation 306 L D English Fig 17.2 Graph displaying ordering of licorice masses from largest to smallest Fig 17.3 Class plots for each licorice-making method Of particular interest in the students’ representations were their inscriptions and accompanying written text For their first representation, 71% of the students annotated their creations, while 83% did so on their second representation Their annotations comprised various approaches to documenting the chances of selecting the different colours, as illustrated in Figs 17.4 and 17.5 Millicent and Greta’s represen- 17 Young Children’s Statistical Literacy in Modelling with Data … Table 17.2 Students’ representations for the chance activity Representation type First representation (%) Second representation (%) Circle graph 38 63 Bar graph 42 21 Grid Picture graph 12 4 4 4 Illustration Text only N 307 24 tations are chosen as examples of ways in which students linked their understanding of chance, fraction, and statistics Millicent (Fig 17.4) explained that her resultant models “tell you that red is out of 12, blue is out of 12, green is out of 12, and yellow is out of 12 and together they add to twelve” It is interesting that Millicent preferred her bar graph to the circle graph, explaining: “A bar graph because it does the total too, as well as everything else it needs to” For Millicent, the circle graph did not appear as effective for displaying the total possible outcomes A desire to display the total possible outcomes is an interesting feature of many of the models created, suggesting further development of statistical literacy That is, without prompting, the children were able to create models that indicated a linking of chance, fraction, and statistical understandings This connected understanding is also evident in Fig 17.5, where Greta used a range of annotations displaying the chances and likelihood of selecting the different coloured counters Although Greta did not accurately display the total possible outcomes on her bar graph, she nevertheless indicated the likelihood of each colour being selected Greta also preferred her bar graph, apparently because of her textual annotations (“The plot because it had it in words”) 17.7 Discussion and Concluding Points This chapter has examined two investigations that revealed 8-year-olds’ statistical literacy in modelling with data and chance Children’s responses to both activities were explored in terms of how they identified variation, made informal inferences, created representations, and interpreted their resultant models Given that the two investigations were the children’s first exposure to modelling with data, their responses suggest they were developing important foundational components of statistical literacy The children could readily identify variation in the data of both investigations, and furthermore, could explain why such variation occurred They recognized why there was reduced variation in the factory made licorice sticks and understood how varia- 308 Fig 17.4 Millicent’s annotations L D English 17 Young Children’s Statistical Literacy in Modelling with Data … Fig 17.5 Greta’s annotations 309 310 L D English tion in their predictions and outcomes of the chance investigation was due largely to the different proportions of their coloured counters Using their understanding of variation as a foundation, the students were able to draw informal inferences regarding licorice stick masses that might be produced in future licorice-making activities Drawing on their understanding of typical and average, as well as their recognition of other factors that could generate variation, the children displayed a degree of uncertainty in drawing conclusions about future stick masses For the probability investigation, three quarters of the children made predictions based on their proportions of coloured counters, while a few other students referred to the random nature of selection, or their preference for a particular colour Nevertheless, the children’s “intuitive ideas” or personal beliefs or perceptions about probability (Hawkins & Kapadia, 1984, p 349) appeared rarely in this particular investigation In contrast to common activities with equally likely outcomes (e.g rolling a die), where an “equiprobability” bias can be present (e.g Khazanov, 2008), the nature of this chance investigation enabled students to appreciate the variation in outcomes possible Furthermore, students’ control over their initial counter selection appeared to facilitate their understanding that predictions and outcomes can vary, and that the former does not guarantee the latter An appreciation of the relationship between variation and expectation is critical in students’ development of formal probability models (English & Watson, 2016) One of the interesting findings from the children’s representations was the greater variety of models generated from the chance investigation, in contrast to the common use of bar graph models for the licorice-making Despite the preferred use of bar graphs, the students displayed different approaches to organizing and structuring their data in the licorice-making investigation, and furthermore, could identify the data distributions of the whole-class models for the two methods Their identification of how the distributions differed reflects Konold and Pollatsek’s (2002) notion of signal in noise, where the handmade data showed more “noise” than the factory made In describing and comparing the two data distributions, students identified their features in terms of familiar contexts (e.g a “bed” and “boxes in a storage room”) as well as in terms of statistical notions such as data clusters and typical mass values The students’ representations for the models produced from the chance investigation varied considerably There was a greater use of circle graphs than in the previous investigation, even though the children had not been formally introduced to this representational form Their use of inscriptions, indicating a linking of mathematics, statistics, and probability understandings, was unexpected as, again, they had not received formal instruction in creating such models Furthermore, with all but a couple of children able to generate more than one representational model to display the same data, it appeared the students had developed the metarepresentational competence identified by diSessa (2004) The children’s identification of the representational model that more effectively conveyed the chance outcomes further indicated their conceptual linking of chance and statistical notions For example, for many children, their desired models needed to indicate clearly the total possible outcomes, which was taken into consideration in model generation As illustrated in Figs 17.4 and 17.5, children frequently included an additional bar to display the total 17 Young Children’s Statistical Literacy in Modelling with Data … 311 outcomes or to document the chances of selecting each colour Such an inclusion was unexpected and suggests these children had developed a solid grasp of chance outcomes expressed in fraction form Those students who preferred the circle model gave reasons such as “…because you can easily tell that red and blue are even, and yellow and green are even, and red and blue have more counters”, “…because it tells you about how many counters you have and it shows the chance like most likely, least likely and equal”, and “I think pie graph because it explains the chance in two ways: in fractions and in chance out of twelve” Children’s responses to both investigations highlight the learning affordances generated when students actually create their own data, experience variation “in action”, make predictions based on their findings (rather than someone’s else’s), and generate their own models to convey their investigative “story” More opportunities that capitalize on, and advance, young children’s learning potential in early statistics and probability are clearly warranted, especially when research is revealing the enhanced mathematical skills of today’s beginning school students in contrast to previous years (Bassok & Latham, 2017) Acknowledgments This study was supported by funding from the Australian Research Council (ARC; DP150100120) Views expressed in this paper are those of the author and not the ARC Collaborators (Jane Watson and Noleine Fitzallen) are acknowledged for their creation of the statistics investigation, while senior research assistant, Jo Macri’s contribution to data collection and recording for both investigations is also gratefully acknowledged References Bassok, D., & Latham, S (2017) Kids today: The rise of children’s academic skills at kindergarten entry Educational Researcher, 46(1), 7–20 Ben-Zvi, D., & Garfield, J (2004) Statistical literacy, reasoning, and thinking: Goals, definitions, and challenges In D Ben-Zvi & J Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp 3–25) Dordrecht, The Netherlands: Kluwer Cobb, P., Jackson, K., & Dunlap, C (2016) Design research: An analysis and critique In L D English & D Kirshner (Eds.), Handbook of international research in mathematics education (3rd ed., pp 481–503) New York, NY: Routledge diSessa, A A (2004) Meta-representation: Native competence and targets for instruction Cognition and Instruction, 22(3), 293–331 diSessa, A A (2018) Computational literacy and “The Big Picture” concerning computers in mathematics education Mathematical Thinking and Learning, 20(1), 3–31 Doerr, H M., Delmas, R., & Makar, K (2017) A modeling approach to the development of students’ informal inferential reasoning Statistics Education Research Journal, 16(1) http://iase-web.or g/Publications.php?p=SERJ English, L D (2010) Young children’s early modelling with data Mathematics Education Research Journal, 22(2), 24–47 English, L D (2013) Reconceptualising statistical learning in the early years In L D English & J Mulligan (Eds.), Reconceptualising early mathematics learning (pp 67–82) Dordrecht: Springer English, L D (2014) Statistics at Play Teaching Children Mathematics, 21(1), 36–45) 312 L D English English, L., & Watson, J (2013) Beginning inference in fourth grade: Exploring variation in measurement In V Steinle, L Ball, & C Bardini (Eds.), Mathematics education: Yesterday, today, and tomorrow In Proceedings of the 36th Annual Conference of the Mathematics Education Research Group of Australasia (Vol 1, pp 274–281) Melbourne, Victoria: Mathematics Education Research Group of Australasia Retrieved from h_et_al_MERGA36–2013.pdf English, L D., & Watson, J M (2016) Development of probabilistic understanding in fourth grade Journal for Research in Mathematics Education, 47(1), 27–61 Fielding-Wells, J (2014) Where’s your evidence? Challenging young students’ equiprobability bias through argumentation In K Makar, B de Sousa, & R Gould (Eds.), Sustainability in statistics education Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA Voorburg, The Netherlands: International Statistical Institute [© 2014 ISI/IASE] Gal, I (2002) Adults’ statistical literacy: Meanings, components, responsibilities International Statistical Review, 70(1), 1–25 Gal, I (2005) Towards “probability literacy” for all citizens: Building blocks and instructional dilemmas In G A Jones (Ed.), Exploring probability in school: Challenges for teaching and learning (pp 39–63) New York, NY: Springer Garfield, J., & Ben-Zvi, D (2008) Developing students’ statistical reasoning: Connecting research and teaching Dordrecht: Springer Hawkins, A S., & Kapadia, R (1984) Children’s conceptions of probability: A psychological and pedagogical review Educational Studies in Mathematics, 15(4), 349–347 Hestenes, D (2010) Modeling theory for math and science education In R Lesh, P Galbraith, C Hines, & A Hurford (Eds.), Modeling students’ mathematical competencies New York: Springer Jones, G A., Langrall, C W., & Mooney, E S (2007) Research in probability: Responding to classroom realities In F K Lester Jr (Ed.), Second handbook of research on mathematics teaching and learning (Vol 2, pp 909–955) Charlotte, NC: Information Age Kalantzis, M., & Cope, B (2012) Literacies New York: Cambridge University Press Khazanov, L (2008) Addressing students’ misconceptions about probability during the first years of college Mathematics and Computer Education, 42(3), 180–192 Konold, C., & Pollatsek, A (2002) Data analysis as the search for signals in noisy processes Journal for Research in Mathematics Education, 33(4), 259–289 Lehrer, R (2011, April) Learning to reason about variability and chance by inventing measures and models Paper presented at the annual meeting of the National Association for Research in Science Teaching, Orlando, FL Lehrer, R &, English, L D (2018) Modeling variability In Ben-Zvi, D., Garfield, J., & Makar, K (Eds.) International handbook of research in statistics education (pp 229–260) Dordrecht: Springer Lesh, R., & Lehrer, R (2000) Iterative refinement cycles for videotape analyses of conceptual change In R Lesh & A Kelly (Eds.) Research design in mathematics and science education (pp 665–708) Hillsdale, NJ: Erlbaum Lehrer, R., & Schauble, L (2000) Inventing data structures for representational purposes: Elementary grade students’ classification models Mathematical Thinking and Learning, 2, 49–72 Lehrer, R., & Schauble, L (2005) Developing modeling and argument in the elementary grades In T A Romberg, T P Carpenter, & F Dremock (Eds.), Understanding mathematics and science matters (pp 29–53) Mahwah, NJ: Erlbaum Lehrer, R., & Schauble, L (2007) Contrasting emerging conceptions of distribution in contexts of error and natural variation In M C Lovett & P Shah (Eds.), Thinking with data (pp 149–176) Mahwah, NJ: Erlbaum Makar, K (2016) Developing young children’s emergent inferential practices in statistics Mathematical Thinking and Learning, 18(1), 1–24 17 Young Children’s Statistical Literacy in Modelling with Data … 313 Makar, K., & Confrey, J (2005) “Variation-talk”: Articulating meaning in statistics Statistics Education Research Journal, 4(1), 27–54 Makar, K., & Rubin, A (2009) A framework for thinking about informal statistical inference Statistics Education Research Journal, 8(1), 82–105 Makar, K., Bakker, A., & Ben-Zvi, D (2011) The reasoning behind informal statistical inference Mathematical Thinking and Learning, 13(1–2), 152–173 Murphy, S J (2001) Probably Pistachio St Louis, MO: Turtleback Books Patton, M (2002) Qualitative research and evaluation methods (3rd ed.) Thousand Oaks: Sage Watson, J M (2006) Statistical literacy at school: Growth and goals Mahwah, NJ: Lawrence Erlbaum Watson, J., Skalicky, J., Fitzallen, N., & Wright, S (2009) Licorice production and manufacturing: All-sorts of practical applications for statistics Australian primary Mathematics Curriculum, 14(3), 4–13 ... theory and research underlying early statistics learning It gives valuable insights into contemporary and future trends and issues related to early statistics education, informing best practices in. .. statistical and probabilistic concepts in educational settings By providing valuable insights into contemporary and future trends and issues related to the development of early thinking about data and. .. developments and approaches to probability and statistics in early childhood and primary education (ages 3–10) It has collected incisive contributions from leading researchers and practitioners internationally,
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Xem thêm: Statistics in Early Childhood and Primary Education, Statistics in Early Childhood and Primary Education, 2 Children’s Probabilistic Thinking: From Piaget and Fischbein to Contemporary Studies, 3 Probability in Early Childhood Educational Practice, 2 Combinatorial, Probabilistic and Statistical Thinking in Childhood, 4 Errors in Classification Carried Out by Children, 4 Graphs in the Spanish Textbooks and External Assessment Tests, 2 Connecting to Statistics: Real-World Problem Solving as Task and Data Context, 5 Children’s Spontaneous Responses, Questions and Comments, 6 Characteristics of Picture Story Books that Stimulate Interest in the Data Context, 7 Implications for Teacher Education and Future Research, 3 Design and Implementation of Activities to Enhance Early Statistical Reasoning and to Lead Grade 4 Students to Comparing Groups, 4 Study of Primary School Students’ Statistical Reasoning When Comparing Groups After Attending to Our Activities

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